Revisiting Event Study Designs: Robust and Efficient Estimation

Kirill Borusyak, Xavier Jaravel and Jann Spiess

No 17247, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation†form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions. Extensions include time-varying controls, triple-differences, and certain non-binary treatments. We show the practical relevance of these insights in a simulation study and an application. Studying the consumption response to tax rebates in the United States, we find that the notional marginal propensity to consume is between 8 and 11 percent in the first quarter — about half as large as benchmark estimates used to calibrate macroeconomic models — and predominantly occurs in the first month after the rebate.

Keywords: Difference-in-differences; Event study; Imputation estimator; Panel data (search for similar items in EconPapers)
JEL-codes: C21 C23 E62 (search for similar items in EconPapers)
Date: 2022-04
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Related works:
Journal Article: Revisiting Event-Study Designs: Robust and Efficient Estimation (2024)
Working Paper: Revisiting Event Study Designs: Robust and Efficient Estimation (2024)
Working Paper: Revisiting event-study designs: robust and efficient estimation (2024)
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